%0 Conference Proceedings
%T SocialAuth: Designing Touch Behavioral Smartphone User Authentication Based on Social Networking Applications
%+ Department of Applied Mathematics and Computer Science [Lyngby] (DTU Compute)
%+ Department of Computer Science [Hong Kong]
%+ CyberTree Research Institute
%+ Singapore University of Technology and Design (SUTD)
%A Meng, Weizhi
%A Li, Wenjuan
%A Jiang, Lijun
%A Zhou, Jianying
%Z Part 3: Organizational and Behavioral
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 34th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC)
%C Lisbon, Portugal
%Y Gurpreet Dhillon
%Y Fredrik Karlsson
%Y Karin Hedström
%Y André Zúquete
%I Springer International Publishing
%3 ICT Systems Security and Privacy Protection
%V AICT-562
%P 180-193
%8 2019-06-25
%D 2019
%R 10.1007/978-3-030-22312-0_13
%K Behavioral user authentication
%K Touch gestures
%K Usable security
%K Smartphone security
%K Social networking
%K Machine learning
%Z Computer Science [cs]Conference papers
%X Modern smartphones expressed an exponential growth and have become a personal assistant in people’s daily lives, i.e., keeping connected with peers. Users are willing to store their personal data even sensitive information on the phones, making these devices an attractive target for cyber-criminals. Due to the limitations of traditional authentication methods like Personal Identification Number (PIN), research has been moved to the design of touch behavioral authentication on smartphones. However, how to design a robust behavioral authentication in a long-term period remains a challenge due to behavioral inconsistency. In this work, we advocate that touch gestures could become more consistent when users interact with specific applications. In this work, we focus on social networking applications and design a touch behavioral authentication scheme called SocialAuth. In the evaluation, we conduct a user study with 50 participants and demonstrate that touch behavioral deviation under our scheme could be significantly decreased and kept relatively stable even after a long-term period, i.e., a single SVM classifier could achieve an average error rate of about 3.1% and 3.7% before and after two weeks, respectively.
%G English
%Z TC 11
%2 https://inria.hal.science/hal-03744302/document
%2 https://inria.hal.science/hal-03744302/file/485650_1_En_13_Chapter.pdf
%L hal-03744302
%U https://inria.hal.science/hal-03744302
%~ LORIA2
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC11
%~ IFIP-SEC
%~ IFIP-AICT-562